Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations35064
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 MiB
Average record size in memory311.3 B

Variable types

DateTime1
Numeric11
Categorical5

Alerts

clouds_all is highly overall correlated with weather_description and 1 other fieldsHigh correlation
humidity is highly overall correlated with temp and 2 other fieldsHigh correlation
rain_1h is highly overall correlated with weather_description and 3 other fieldsHigh correlation
temp is highly overall correlated with humidity and 2 other fieldsHigh correlation
temp_max is highly overall correlated with humidity and 2 other fieldsHigh correlation
temp_min is highly overall correlated with humidity and 2 other fieldsHigh correlation
weather_description is highly overall correlated with clouds_all and 4 other fieldsHigh correlation
weather_icon is highly overall correlated with clouds_all and 4 other fieldsHigh correlation
weather_id is highly overall correlated with rain_1h and 3 other fieldsHigh correlation
weather_main is highly overall correlated with rain_1h and 3 other fieldsHigh correlation
rain_1h is highly imbalanced (76.6%)Imbalance
snow_3h is highly imbalanced (> 99.9%)Imbalance
weather_main is highly imbalanced (51.1%)Imbalance
weather_description is highly imbalanced (54.7%)Imbalance
rain_3h is highly skewed (γ1 = 25.71845945)Skewed
time has 1461 (4.2%) zerosZeros
wind_speed has 2587 (7.4%) zerosZeros
wind_deg has 4439 (12.7%) zerosZeros
rain_3h has 34889 (99.5%) zerosZeros
clouds_all has 20026 (57.1%) zerosZeros

Reproduction

Analysis started2024-09-11 14:31:45.041075
Analysis finished2024-09-11 14:31:59.593439
Duration14.55 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

date
Date

Distinct1462
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size547.9 KiB
Minimum2014-12-31 00:00:00
Maximum2018-12-31 00:00:00
2024-09-11T16:31:59.682403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:59.817859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

time
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:31:59.931185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-09-11T16:32:00.025928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23 1461
 
4.2%
0 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
14 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

temp
Real number (ℝ)

HIGH CORRELATION 

Distinct7272
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.27713
Minimum264.132
Maximum313.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:32:00.131491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum264.132
5-th percentile274.65
Q1281.15
median286.93
Q3294.95
95-th percentile305.15
Maximum313.33
Range49.198
Interquartile range (IQR)13.8

Descriptive statistics

Standard deviation9.3269934
Coefficient of variation (CV)0.032354261
Kurtosis-0.61872032
Mean288.27713
Median Absolute Deviation (MAD)6.63
Skewness0.32550945
Sum10108149
Variance86.992806
MonotonicityNot monotonic
2024-09-11T16:32:00.249885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
281.15 262
 
0.7%
283.15 233
 
0.7%
280.15 214
 
0.6%
282.15 207
 
0.6%
278.15 187
 
0.5%
285.15 183
 
0.5%
284.15 183
 
0.5%
286.15 180
 
0.5%
287.15 171
 
0.5%
279.15 168
 
0.5%
Other values (7262) 33076
94.3%
ValueCountFrequency (%)
264.132 1
 
< 0.1%
264.428 2
< 0.1%
265.091 2
< 0.1%
265.261 3
< 0.1%
265.442 3
< 0.1%
265.6245 1
 
< 0.1%
265.638 1
 
< 0.1%
265.902 3
< 0.1%
266.0235 1
 
< 0.1%
266.024 3
< 0.1%
ValueCountFrequency (%)
313.33 1
< 0.1%
313.14 1
< 0.1%
312.94 2
< 0.1%
312.93 1
< 0.1%
312.8 1
< 0.1%
312.76 1
< 0.1%
312.74 1
< 0.1%
312.72 1
< 0.1%
312.54 1
< 0.1%
312.38 1
< 0.1%

temp_min
Real number (ℝ)

HIGH CORRELATION 

Distinct4370
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean287.05183
Minimum264.132
Maximum312.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:32:00.365007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum264.132
5-th percentile273.15
Q1280.15
median286.14
Q3293.71
95-th percentile303.15
Maximum312.15
Range48.018
Interquartile range (IQR)13.56

Descriptive statistics

Standard deviation9.1787528
Coefficient of variation (CV)0.031975942
Kurtosis-0.66599967
Mean287.05183
Median Absolute Deviation (MAD)6.99
Skewness0.29660208
Sum10065186
Variance84.249504
MonotonicityNot monotonic
2024-09-11T16:32:00.485302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
281.15 1087
 
3.1%
280.15 1084
 
3.1%
283.15 1056
 
3.0%
279.15 1005
 
2.9%
282.15 970
 
2.8%
278.15 950
 
2.7%
273.15 920
 
2.6%
284.15 872
 
2.5%
293.15 870
 
2.5%
285.15 829
 
2.4%
Other values (4360) 25421
72.5%
ValueCountFrequency (%)
264.132 1
 
< 0.1%
264.428 2
< 0.1%
265.091 2
< 0.1%
265.261 3
< 0.1%
265.442 3
< 0.1%
265.6245 1
 
< 0.1%
265.638 1
 
< 0.1%
265.902 3
< 0.1%
266.0235 1
 
< 0.1%
266.024 3
< 0.1%
ValueCountFrequency (%)
312.15 2
 
< 0.1%
311.15 4
 
< 0.1%
310.859 3
 
< 0.1%
310.75 1
 
< 0.1%
310.725 3
 
< 0.1%
310.65 1
 
< 0.1%
310.25 1
 
< 0.1%
310.15 35
0.1%
309.95 2
 
< 0.1%
309.85 1
 
< 0.1%

temp_max
Real number (ℝ)

HIGH CORRELATION 

Distinct4420
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.37196
Minimum264.132
Maximum316.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:32:00.599455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum264.132
5-th percentile275.15
Q1282.15
median288.15
Q3296.15
95-th percentile307.15
Maximum316.48
Range52.348
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.6819398
Coefficient of variation (CV)0.033458458
Kurtosis-0.636581
Mean289.37196
Median Absolute Deviation (MAD)7
Skewness0.32461809
Sum10146539
Variance93.739958
MonotonicityNot monotonic
2024-09-11T16:32:00.791561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
283.15 1111
 
3.2%
285.15 1100
 
3.1%
284.15 1083
 
3.1%
282.15 1043
 
3.0%
286.15 1018
 
2.9%
281.15 1014
 
2.9%
287.15 964
 
2.7%
288.15 861
 
2.5%
280.15 851
 
2.4%
293.15 769
 
2.2%
Other values (4410) 25250
72.0%
ValueCountFrequency (%)
264.132 1
 
< 0.1%
264.428 2
< 0.1%
265.091 2
< 0.1%
265.261 3
< 0.1%
265.442 3
< 0.1%
265.6245 1
 
< 0.1%
265.638 1
 
< 0.1%
265.902 3
< 0.1%
266.0235 1
 
< 0.1%
266.024 3
< 0.1%
ValueCountFrequency (%)
316.48 2
 
< 0.1%
315.37 1
 
< 0.1%
315.15 1
 
< 0.1%
314.82 2
 
< 0.1%
314.75 1
 
< 0.1%
314.65 1
 
< 0.1%
314.55 1
 
< 0.1%
314.25 2
 
< 0.1%
314.15 9
< 0.1%
313.71 1
 
< 0.1%

pressure
Real number (ℝ)

Distinct115
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1011.7811
Minimum927
Maximum1042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:32:00.903045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum927
5-th percentile957
Q11013
median1017
Q31022
95-th percentile1030
Maximum1042
Range115
Interquartile range (IQR)9

Descriptive statistics

Standard deviation20.239961
Coefficient of variation (CV)0.020004288
Kurtosis3.7928449
Mean1011.7811
Median Absolute Deviation (MAD)5
Skewness-2.1619359
Sum35477093
Variance409.65602
MonotonicityNot monotonic
2024-09-11T16:32:01.029451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1018 2424
 
6.9%
1017 2330
 
6.6%
1019 2229
 
6.4%
1016 2203
 
6.3%
1020 2022
 
5.8%
1015 1912
 
5.5%
1021 1656
 
4.7%
1014 1493
 
4.3%
1022 1452
 
4.1%
1023 1262
 
3.6%
Other values (105) 16081
45.9%
ValueCountFrequency (%)
927 1
 
< 0.1%
928 12
< 0.1%
929 4
 
< 0.1%
930 6
 
< 0.1%
931 8
 
< 0.1%
932 6
 
< 0.1%
933 14
< 0.1%
934 20
0.1%
935 11
< 0.1%
936 13
< 0.1%
ValueCountFrequency (%)
1042 2
 
< 0.1%
1040 1
 
< 0.1%
1039 2
 
< 0.1%
1038 6
 
< 0.1%
1037 44
 
0.1%
1036 72
 
0.2%
1035 113
 
0.3%
1034 182
0.5%
1033 288
0.8%
1032 322
0.9%

humidity
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.725331
Minimum0
Maximum100
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:32:01.155166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q138
median59
Q381
95-th percentile96
Maximum100
Range100
Interquartile range (IQR)43

Descriptive statistics

Standard deviation24.883323
Coefficient of variation (CV)0.42372384
Kurtosis-1.1542359
Mean58.725331
Median Absolute Deviation (MAD)22
Skewness-0.061099075
Sum2059145
Variance619.17975
MonotonicityNot monotonic
2024-09-11T16:32:01.273977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 2006
 
5.7%
100 1480
 
4.2%
87 1459
 
4.2%
81 977
 
2.8%
76 723
 
2.1%
71 633
 
1.8%
86 585
 
1.7%
66 575
 
1.6%
62 559
 
1.6%
82 516
 
1.5%
Other values (86) 25551
72.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
6 3
 
< 0.1%
7 8
 
< 0.1%
8 17
 
< 0.1%
9 34
 
0.1%
10 52
 
0.1%
11 89
0.3%
12 78
0.2%
13 149
0.4%
14 191
0.5%
ValueCountFrequency (%)
100 1480
4.2%
99 65
 
0.2%
98 76
 
0.2%
97 104
 
0.3%
96 173
 
0.5%
95 104
 
0.3%
94 144
 
0.4%
93 2006
5.7%
92 211
 
0.6%
91 132
 
0.4%

wind_speed
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4338068
Minimum0
Maximum18
Zeros2587
Zeros (%)7.4%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:32:01.375198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum18
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9730405
Coefficient of variation (CV)0.81068085
Kurtosis2.3174967
Mean2.4338068
Median Absolute Deviation (MAD)1
Skewness1.4113511
Sum85339
Variance3.892889
MonotonicityNot monotonic
2024-09-11T16:32:01.465837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 12052
34.4%
2 7751
22.1%
3 4483
 
12.8%
4 3004
 
8.6%
0 2587
 
7.4%
5 2151
 
6.1%
6 1331
 
3.8%
7 825
 
2.4%
8 451
 
1.3%
9 251
 
0.7%
Other values (8) 178
 
0.5%
ValueCountFrequency (%)
0 2587
 
7.4%
1 12052
34.4%
2 7751
22.1%
3 4483
 
12.8%
4 3004
 
8.6%
5 2151
 
6.1%
6 1331
 
3.8%
7 825
 
2.4%
8 451
 
1.3%
9 251
 
0.7%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 2
 
< 0.1%
15 3
 
< 0.1%
14 4
 
< 0.1%
13 6
 
< 0.1%
12 18
 
0.1%
11 50
 
0.1%
10 94
 
0.3%
9 251
0.7%
8 451
1.3%

wind_deg
Real number (ℝ)

ZEROS 

Distinct361
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.36391
Minimum0
Maximum360
Zeros4439
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:32:01.580826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median190
Q3270
95-th percentile356.85
Maximum360
Range360
Interquartile range (IQR)220

Descriptive statistics

Standard deviation121.94544
Coefficient of variation (CV)0.70340732
Kurtosis-1.3247779
Mean173.36391
Median Absolute Deviation (MAD)111
Skewness-0.030548285
Sum6078832
Variance14870.69
MonotonicityNot monotonic
2024-09-11T16:32:01.712789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4439
 
12.7%
360 1721
 
4.9%
350 1646
 
4.7%
340 1238
 
3.5%
220 1074
 
3.1%
230 1054
 
3.0%
10 1014
 
2.9%
240 934
 
2.7%
210 850
 
2.4%
330 750
 
2.1%
Other values (351) 20344
58.0%
ValueCountFrequency (%)
0 4439
12.7%
1 13
 
< 0.1%
2 12
 
< 0.1%
3 14
 
< 0.1%
4 20
 
0.1%
5 27
 
0.1%
6 25
 
0.1%
7 24
 
0.1%
8 18
 
0.1%
9 23
 
0.1%
ValueCountFrequency (%)
360 1721
4.9%
359 10
 
< 0.1%
358 8
 
< 0.1%
357 15
 
< 0.1%
356 12
 
< 0.1%
355 34
 
0.1%
354 5
 
< 0.1%
353 28
 
0.1%
352 9
 
< 0.1%
351 11
 
< 0.1%

rain_1h
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0.0
32407 
0.3
 
1665
0.9
 
932
3.0
 
60

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters105192
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 32407
92.4%
0.3 1665
 
4.7%
0.9 932
 
2.7%
3.0 60
 
0.2%

Length

2024-09-11T16:32:01.839717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-11T16:32:01.949777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 32407
92.4%
0.3 1665
 
4.7%
0.9 932
 
2.7%
3.0 60
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 67471
64.1%
. 35064
33.3%
3 1725
 
1.6%
9 932
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 67471
64.1%
. 35064
33.3%
3 1725
 
1.6%
9 932
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 67471
64.1%
. 35064
33.3%
3 1725
 
1.6%
9 932
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 67471
64.1%
. 35064
33.3%
3 1725
 
1.6%
9 932
 
0.9%

rain_3h
Real number (ℝ)

SKEWED  ZEROS 

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00013295688
Minimum0
Maximum0.1
Zeros34889
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:32:02.046003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.1
Range0.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0025906774
Coefficient of variation (CV)19.485095
Kurtosis751.72144
Mean0.00013295688
Median Absolute Deviation (MAD)0
Skewness25.718459
Sum4.662
Variance6.7116092 × 10-6
MonotonicityNot monotonic
2024-09-11T16:32:02.160723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 34889
99.5%
0.015 22
 
0.1%
0.005 18
 
0.1%
0.01 16
 
< 0.1%
0.012 10
 
< 0.1%
0.025 8
 
< 0.1%
0.05 6
 
< 0.1%
0.0025 6
 
< 0.1%
0.03 6
 
< 0.1%
0.037 6
 
< 0.1%
Other values (28) 77
 
0.2%
ValueCountFrequency (%)
0 34889
99.5%
0.002 6
 
< 0.1%
0.0025 6
 
< 0.1%
0.003 5
 
< 0.1%
0.005 18
 
0.1%
0.006 4
 
< 0.1%
0.0075 3
 
< 0.1%
0.008 3
 
< 0.1%
0.01 16
 
< 0.1%
0.0105 2
 
< 0.1%
ValueCountFrequency (%)
0.1 2
< 0.1%
0.095 2
< 0.1%
0.09 1
 
< 0.1%
0.087 3
< 0.1%
0.085 4
< 0.1%
0.08 2
< 0.1%
0.075 1
 
< 0.1%
0.067 2
< 0.1%
0.065 4
< 0.1%
0.055 4
< 0.1%

snow_3h
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0.0
35063 
1.0490000000000002
 
1

Length

Max length18
Median length3
Mean length3.0004278
Min length3

Characters and Unicode

Total characters105207
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 35063
> 99.9%
1.0490000000000002 1
 
< 0.1%

Length

2024-09-11T16:32:02.276768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-11T16:32:02.380267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 35063
> 99.9%
1.0490000000000002 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 70139
66.7%
. 35064
33.3%
1 1
 
< 0.1%
4 1
 
< 0.1%
9 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105207
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 70139
66.7%
. 35064
33.3%
1 1
 
< 0.1%
4 1
 
< 0.1%
9 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105207
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 70139
66.7%
. 35064
33.3%
1 1
 
< 0.1%
4 1
 
< 0.1%
9 1
 
< 0.1%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105207
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 70139
66.7%
. 35064
33.3%
1 1
 
< 0.1%
4 1
 
< 0.1%
9 1
 
< 0.1%
2 1
 
< 0.1%

clouds_all
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.661334
Minimum0
Maximum100
Zeros20026
Zeros (%)57.1%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:32:02.489375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q340
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation29.656482
Coefficient of variation (CV)1.4353615
Kurtosis-0.054374422
Mean20.661334
Median Absolute Deviation (MAD)0
Skewness1.192667
Sum724469
Variance879.50693
MonotonicityNot monotonic
2024-09-11T16:32:02.636459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20026
57.1%
20 3998
 
11.4%
75 3559
 
10.2%
40 2738
 
7.8%
90 926
 
2.6%
92 431
 
1.2%
8 270
 
0.8%
12 221
 
0.6%
24 190
 
0.5%
32 176
 
0.5%
Other values (78) 2529
 
7.2%
ValueCountFrequency (%)
0 20026
57.1%
2 26
 
0.1%
3 2
 
< 0.1%
4 80
 
0.2%
5 26
 
0.1%
6 50
 
0.1%
7 1
 
< 0.1%
8 270
 
0.8%
9 4
 
< 0.1%
10 101
 
0.3%
ValueCountFrequency (%)
100 26
 
0.1%
96 1
 
< 0.1%
95 2
 
< 0.1%
92 431
1.2%
91 10
 
< 0.1%
90 926
2.6%
89 8
 
< 0.1%
88 153
 
0.4%
87 3
 
< 0.1%
86 12
 
< 0.1%

weather_id
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean769.42086
Minimum200
Maximum804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size547.9 KiB
2024-09-11T16:32:02.760101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile500
Q1800
median800
Q3801
95-th percentile803
Maximum804
Range604
Interquartile range (IQR)1

Descriptive statistics

Standard deviation98.393706
Coefficient of variation (CV)0.12788022
Kurtosis11.237701
Mean769.42086
Median Absolute Deviation (MAD)0
Skewness-3.3794313
Sum26978973
Variance9681.3214
MonotonicityNot monotonic
2024-09-11T16:32:02.886039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
800 20352
58.0%
801 4208
 
12.0%
802 3054
 
8.7%
803 2888
 
8.2%
500 1356
 
3.9%
701 615
 
1.8%
501 592
 
1.7%
804 493
 
1.4%
741 466
 
1.3%
300 299
 
0.9%
Other values (18) 741
 
2.1%
ValueCountFrequency (%)
200 4
 
< 0.1%
201 7
 
< 0.1%
202 1
 
< 0.1%
211 152
 
0.4%
300 299
 
0.9%
301 82
 
0.2%
302 2
 
< 0.1%
310 2
 
< 0.1%
311 2
 
< 0.1%
500 1356
3.9%
ValueCountFrequency (%)
804 493
 
1.4%
803 2888
 
8.2%
802 3054
 
8.7%
801 4208
 
12.0%
800 20352
58.0%
741 466
 
1.3%
721 16
 
< 0.1%
701 615
 
1.8%
616 2
 
< 0.1%
615 7
 
< 0.1%

weather_main
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
clear
20352 
clouds
10643 
rain
2381 
mist
 
615
fog
 
466
Other values (4)
 
607

Length

Max length12
Median length5
Mean length5.2447239
Min length3

Characters and Unicode

Total characters183901
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclear
2nd rowclear
3rd rowclear
4th rowclear
5th rowclear

Common Values

ValueCountFrequency (%)
clear 20352
58.0%
clouds 10643
30.4%
rain 2381
 
6.8%
mist 615
 
1.8%
fog 466
 
1.3%
drizzle 387
 
1.1%
thunderstorm 164
 
0.5%
snow 40
 
0.1%
haze 16
 
< 0.1%

Length

2024-09-11T16:32:02.999792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-11T16:32:03.114623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
clear 20352
58.0%
clouds 10643
30.4%
rain 2381
 
6.8%
mist 615
 
1.8%
fog 466
 
1.3%
drizzle 387
 
1.1%
thunderstorm 164
 
0.5%
snow 40
 
0.1%
haze 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
l 31382
17.1%
c 30995
16.9%
r 23448
12.8%
a 22749
12.4%
e 20919
11.4%
s 11462
 
6.2%
o 11313
 
6.2%
d 11194
 
6.1%
u 10807
 
5.9%
i 3383
 
1.8%
Other values (8) 6249
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 183901
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 31382
17.1%
c 30995
16.9%
r 23448
12.8%
a 22749
12.4%
e 20919
11.4%
s 11462
 
6.2%
o 11313
 
6.2%
d 11194
 
6.1%
u 10807
 
5.9%
i 3383
 
1.8%
Other values (8) 6249
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 183901
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 31382
17.1%
c 30995
16.9%
r 23448
12.8%
a 22749
12.4%
e 20919
11.4%
s 11462
 
6.2%
o 11313
 
6.2%
d 11194
 
6.1%
u 10807
 
5.9%
i 3383
 
1.8%
Other values (8) 6249
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 183901
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 31382
17.1%
c 30995
16.9%
r 23448
12.8%
a 22749
12.4%
e 20919
11.4%
s 11462
 
6.2%
o 11313
 
6.2%
d 11194
 
6.1%
u 10807
 
5.9%
i 3383
 
1.8%
Other values (8) 6249
 
3.4%

weather_description
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
sky is clear
20352 
few clouds
4208 
scattered clouds
3054 
broken clouds
2888 
light rain
 
1356
Other values (25)
3206 

Length

Max length28
Median length12
Mean length12.132586
Min length3

Characters and Unicode

Total characters425417
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsky is clear
2nd rowsky is clear
3rd rowsky is clear
4th rowsky is clear
5th rowsky is clear

Common Values

ValueCountFrequency (%)
sky is clear 20352
58.0%
few clouds 4208
 
12.0%
scattered clouds 3054
 
8.7%
broken clouds 2888
 
8.2%
light rain 1356
 
3.9%
mist 615
 
1.8%
moderate rain 592
 
1.7%
overcast clouds 493
 
1.4%
fog 466
 
1.3%
light intensity drizzle 299
 
0.9%
Other values (20) 741
 
2.1%

Length

2024-09-11T16:32:03.232304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sky 20352
22.6%
clear 20352
22.6%
is 20352
22.6%
clouds 10643
11.8%
few 4208
 
4.7%
scattered 3054
 
3.4%
broken 2888
 
3.2%
rain 2406
 
2.7%
light 1802
 
2.0%
mist 615
 
0.7%
Other values (14) 3386
 
3.8%

Most occurring characters

ValueCountFrequency (%)
s 56576
13.3%
54994
12.9%
e 36726
8.6%
c 34542
8.1%
l 33186
7.8%
r 31169
 
7.3%
i 27094
 
6.4%
a 26983
 
6.3%
k 23240
 
5.5%
y 21171
 
5.0%
Other values (15) 79736
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 425417
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 56576
13.3%
54994
12.9%
e 36726
8.6%
c 34542
8.1%
l 33186
7.8%
r 31169
 
7.3%
i 27094
 
6.4%
a 26983
 
6.3%
k 23240
 
5.5%
y 21171
 
5.0%
Other values (15) 79736
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 425417
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 56576
13.3%
54994
12.9%
e 36726
8.6%
c 34542
8.1%
l 33186
7.8%
r 31169
 
7.3%
i 27094
 
6.4%
a 26983
 
6.3%
k 23240
 
5.5%
y 21171
 
5.0%
Other values (15) 79736
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 425417
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 56576
13.3%
54994
12.9%
e 36726
8.6%
c 34542
8.1%
l 33186
7.8%
r 31169
 
7.3%
i 27094
 
6.4%
a 26983
 
6.3%
k 23240
 
5.5%
y 21171
 
5.0%
Other values (15) 79736
18.7%

weather_icon
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
01n
9437 
01d
9131 
02d
2458 
02n
1653 
04n
1576 
Other values (18)
10809 

Length

Max length3
Median length3
Mean length2.9054586
Min length2

Characters and Unicode

Total characters101877
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01n
2nd row01n
3rd row01n
4th row01n
5th row01n

Common Values

ValueCountFrequency (%)
01n 9437
26.9%
01d 9131
26.0%
02d 2458
 
7.0%
02n 1653
 
4.7%
04n 1576
 
4.5%
03d 1524
 
4.3%
04d 1301
 
3.7%
01 1268
 
3.6%
03n 1169
 
3.3%
10n 722
 
2.1%
Other values (13) 4825
13.8%

Length

2024-09-11T16:32:03.347104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01n 9437
26.9%
01d 9131
26.0%
02d 2458
 
7.0%
02n 1653
 
4.7%
04n 1576
 
4.5%
03d 1524
 
4.3%
04d 1301
 
3.7%
01 1268
 
3.6%
03n 1169
 
3.3%
10n 722
 
2.1%
Other values (13) 4825
13.8%

Most occurring characters

ValueCountFrequency (%)
0 34860
34.2%
1 22199
21.8%
d 16286
16.0%
n 15463
15.2%
2 4724
 
4.6%
4 3381
 
3.3%
3 3094
 
3.0%
5 1097
 
1.1%
9 773
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 101877
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34860
34.2%
1 22199
21.8%
d 16286
16.0%
n 15463
15.2%
2 4724
 
4.6%
4 3381
 
3.3%
3 3094
 
3.0%
5 1097
 
1.1%
9 773
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 101877
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34860
34.2%
1 22199
21.8%
d 16286
16.0%
n 15463
15.2%
2 4724
 
4.6%
4 3381
 
3.3%
3 3094
 
3.0%
5 1097
 
1.1%
9 773
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 101877
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34860
34.2%
1 22199
21.8%
d 16286
16.0%
n 15463
15.2%
2 4724
 
4.6%
4 3381
 
3.3%
3 3094
 
3.0%
5 1097
 
1.1%
9 773
 
0.8%

Interactions

2024-09-11T16:31:57.829600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:46.283645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:47.451734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:48.638070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.722312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:50.777969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:51.972959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:53.092070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:54.391079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:55.522947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:56.786972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:57.918497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:46.391278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:47.544282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:48.725113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.807114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:50.867758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:52.058865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:53.202370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:54.504411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:55.694379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:56.879233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:58.009635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:46.501904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:47.643850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:48.811172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.896118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:50.961467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:52.147188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:53.304845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:54.600318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:55.795339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:56.971962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:58.102016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:46.594591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:47.751682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:48.905269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.992393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:51.050912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:52.236747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:53.479251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:54.695559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:55.890041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:57.072543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:58.210309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:46.688906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:47.843703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.002532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:50.094062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:51.141769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:52.327275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:53.624217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:54.790575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:55.981886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:57.167307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:58.323956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:46.792006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:48.042216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.103025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:50.186239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:51.242104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:52.430867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:53.740486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:54.887081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:56.075650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:57.262273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:58.428106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:46.884471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:48.137936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.199296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:50.279581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:51.341835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:52.537088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:53.840851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:54.985106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:56.177884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:57.353979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:58.530574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:46.991449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:48.240371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.313038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:50.389597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:51.450436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:52.671228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:53.951768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:55.090246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:56.286268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:57.453736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:58.642245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:47.097884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:48.348072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.421358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:50.488057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:51.553643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:52.773291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:54.056801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:55.193178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:56.394558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:57.552635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:58.752702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:47.223456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:48.451322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.520423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:50.584304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:51.778354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:52.871240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:54.161806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:55.295024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:56.497516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:57.645223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:58.853626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:47.330329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:48.541986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:49.621591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:50.680449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:51.874142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:52.974615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:54.265437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:55.397984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:56.596700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-09-11T16:31:57.733970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-09-11T16:32:03.619094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
clouds_allhumiditypressurerain_1hrain_3hsnow_3htemptemp_maxtemp_mintimeweather_descriptionweather_iconweather_idweather_mainwind_degwind_speed
clouds_all1.0000.438-0.2800.2740.0590.051-0.248-0.265-0.2380.0370.5920.5780.3600.3950.0380.229
humidity0.4381.0000.0020.1810.0270.000-0.776-0.782-0.734-0.2820.1990.240-0.0250.1900.054-0.162
pressure-0.2800.0021.0000.1780.0120.018-0.097-0.073-0.102-0.0450.1590.172-0.0990.145-0.061-0.303
rain_1h0.2740.1810.1781.0000.0000.0310.1090.1100.1130.0390.9620.5680.5800.5580.0660.093
rain_3h0.0590.0270.0120.0001.0000.000-0.032-0.039-0.024-0.0080.0100.0780.0780.0150.0010.014
snow_3h0.0510.0000.0180.0310.0001.0000.0000.0000.0000.0080.0290.0330.0170.0130.0080.000
temp-0.248-0.776-0.0970.109-0.0320.0001.0000.9900.9730.2350.1470.198-0.0020.138-0.0900.126
temp_max-0.265-0.782-0.0730.110-0.0390.0000.9901.0000.9410.2300.1450.206-0.0090.135-0.0910.112
temp_min-0.238-0.734-0.1020.113-0.0240.0000.9730.9411.0000.2200.1470.189-0.0050.139-0.0890.115
time0.037-0.282-0.0450.039-0.0080.0080.2350.2300.2201.0000.0760.2880.0250.0660.0300.183
weather_description0.5920.1990.1590.9620.0100.0290.1470.1450.1470.0761.0000.6101.0001.0000.0910.128
weather_icon0.5780.2400.1720.5680.0780.0330.1980.2060.1890.2880.6101.0000.9230.8250.1330.115
weather_id0.360-0.025-0.0990.5800.0780.017-0.002-0.009-0.0050.0251.0000.9231.0000.9130.0430.106
weather_main0.3950.1900.1450.5580.0150.0130.1380.1350.1390.0661.0000.8250.9131.0000.0890.107
wind_deg0.0380.054-0.0610.0660.0010.008-0.090-0.091-0.0890.0300.0910.1330.0430.0891.0000.270
wind_speed0.229-0.162-0.3030.0930.0140.0000.1260.1120.1150.1830.1280.1150.1060.1070.2701.000

Missing values

2024-09-11T16:31:59.136387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-11T16:31:59.404206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datetimetemptemp_mintemp_maxpressurehumiditywind_speedwind_degrain_1hrain_3hsnow_3hclouds_allweather_idweather_mainweather_descriptionweather_icon
351452014-12-3123267.325267.325267.3259716313090.00.00.00800clearsky is clear01n
351462015-01-010267.325267.325267.3259716313090.00.00.00800clearsky is clear01n
351472015-01-011266.186266.186266.1869716412730.00.00.00800clearsky is clear01n
351482015-01-012266.186266.186266.1869716412730.00.00.00800clearsky is clear01n
351492015-01-013266.186266.186266.1869716412730.00.00.00800clearsky is clear01n
351502015-01-014265.442265.442265.4429726402400.00.00.00800clearsky is clear01n
351512015-01-015265.442265.442265.4429726402400.00.00.00800clearsky is clear01n
351522015-01-016265.442265.442265.4429726402400.00.00.00800clearsky is clear01n
351532015-01-017268.276268.276268.2769746613290.00.00.00800clearsky is clear01d
351542015-01-018268.276268.276268.2769746613290.00.00.00800clearsky is clear01d
datetimetemptemp_mintemp_maxpressurehumiditywind_speedwind_degrain_1hrain_3hsnow_3hclouds_allweather_idweather_mainweather_descriptionweather_icon
714022018-12-3113285.34283.15288.15103124000.00.00.00800clearsky is clear01d
714032018-12-3114286.74285.15288.15103024100.00.00.00800clearsky is clear01d
714042018-12-3115288.12287.15289.15103029100.00.00.00800clearsky is clear01d
714052018-12-3116288.34287.15289.1510302612100.00.00.00800clearsky is clear01d
714062018-12-3117287.76286.15289.1510303011900.00.00.00800clearsky is clear01d
714072018-12-3118283.56282.15285.1510308812800.00.00.00800clearsky is clear01n
714082018-12-3119280.12278.15281.1510315212600.00.00.00800clearsky is clear01n
714092018-12-3120278.15278.15278.1510306513400.00.00.00800clearsky is clear01n
714102018-12-3121276.57276.15277.1510316923400.00.00.00800clearsky is clear01n
714112018-12-3122275.15275.15275.1510317413600.00.00.00800clearsky is clear01n